Rough Sets Clustering and Markov model for Web Access Prediction

Discovering user access patterns from web access log is increasing the importance of information to build up adaptive web server according to the individual user’s behavior. The variety of user behaviors on accessing information also grows, which has a great impact on the network utilization. In this paper, we present a rough set clustering to cluster web transactions from web access logs and using Markov model for next access prediction. Using this approach, users can effectively mine web log records to discover and predict access patterns. We perform experiments using real web trace logs collected from www.dusit.ac.th servers. In order to improve its prediction ration, the model includes a rough sets scheme in which search similarity measure to compute the similarity between two sequences using upper approximation

A heuristic room matching algorithm in generating enhanced initial seed for the university course timetabling problem

The University Course Timetabling Problem (UCTP) such as the curriculum-based course timetabling problem is both an NP-hard and NP-complete scheduling problem.The nature of the problem concerns with the assignment of lecturers-courses to available teaching space in an academic institution.The Curriculum-Based University Course Timetabling Problem (CB-UCTP) has a high conflict-density and searching for an improved solution is not trivial.In this study, the authors propose a heuristic room matching algorithm which improves the seed of the CB-UCTP.The objective is to provide a reasonable search point to carry out any improvement phase and the results obtained indicate that the matching algorithm is able to provide very promising results as the fitness score of the solution is significantly enhanced in a very short period of time

Using Markov Model and Association Rules for Web Access Prediction

Mining user patterns of log file can provide significant and useful informative knowledge. A large amount of research has been done on trying to predict correctly the pages a user will request. This task requires the development of models that can predicts a user’s next request to a web server. In this paper, we propose a method for constructing first-order and second-order Markov models of Web site access prediction based on past visitor behavior and compare it association rules technique. This algorithm has been used to cluster similar transition behaviors for efficient used to further improve the efficiency of prediction. From this comparison we propose a best overall method and empirically test the proposed model on real web logs

Pengaturcaraan basic

Buku ini mengandungi beberapa bab iaitu kandungan, prakata kepada pelajar, prakata kepada tenaga pengajar, bab 1: perkakasan, pengurusan data dan perisian, revolusi komputer, komputer, peranti persisian, konsep sistem komputer, mengurus data untuk mengeluarkan maklumat, dalam komputer, pengaturcaraan dan perisian, sistem pengoperasian: ketua pejabat, bab 2: konsep pengaturcaraan, pengaturcaraan dengan perspektif, penyelesaian masalah dan logik pengaturcaraan, teknik reka bentuk program, apakah program?: konsep dan prinsip pengaturcaraan, menulis program, modul pembelajaran i: asas BASIC, modul pembelajaran ii: permulaan BASIC, modul pembelajaran iii: menambah asas anda, modul pembelajaran iv: mengeluarkan laporan, modul pembelajaran v: menulis program pertanyaan saling tindak, modul pembelajaran vi: menggunakan fungsi pratakrif, modul pembelajaran vii: pengurusan data cakera, modul pembelajaran viii: ciri-ciri BASIC lanjutan, modul pembelajaran ix: ans BASIC "baru", glosari, indeks

Mining Usage Web Log Via Independent Component Analysis And Rough Fuzzy

In the past few years, web usage mining techniques have grown rapidly together with the explosive growth of the web, both in the research and commercial areas. Web Usage Mining is that area of Web Mining which deals with the extraction of interesting knowledge from logging information produced by Web servers. A challenge in web classification is how to deal with the high dimensionality of the feature space. In this paper we present Independent Component Analysis (ICA) for feature selection and using Rough Fuzzy for clustering web user sessions. Our experiments indicate can improve the predictive performance when the original feature set for representing web log is large and can handling the different groups of uncertainties/impreciseness accuracy

Independent Component Analysis And Rough Fuzzy Based Approach To Web Usage Mining

Web Usage Mining is that area of Web Mining which deals with the extraction of interesting knowledge from logging information produced by Web servers. A challenge in web classification is how to deal with the high dimensionality of the feature space. In this paper we present Independent Component Analysis (ICA) for feature selection and using Rough Fuzzy for clustering web user sessions. It aims at discovery of trends and regularities in web users’ access patterns. ICA is a very general-purpose statistical technique in which observed random data are linearly transformed into components that are maximally independent from each other, and simultaneously have “interesting� distributions. Our experiments indicate can improve the predictive performance when the original feature set for representing web log is large and can handling the different groups of uncertainties/ impreciseness accuracy

An alternative model for ERP maintenance strategy

For the past few years, it has been possible to buy a business application including transaction processing systems for such tasks as accounting, manufacturing, or human resources as a packaged product. Packages to do this collection of work are generally referred to as Enterprise Resource Planning (ERP) systems. Most ERP systems are huge because of the diversity of tasks they must perform. The ERP systems are providing an integration of several tasks, and the flexibility to perform those tasks at enterprises with vastly varying needs. But, only few of these ERP systems developed have actually considered maintenance strategies. Maintenance is a complex process that is triggered by planned periodic repair (scheduled or planned maintenance), equipment breakdown or deterioration indicated by a monitored parameter (unplanned or emergency maintenance). This process includes planning, scheduling, monitoring, quality assurance and the development of necessary resources such as workshop, labor, machines, equipment, tools, spare parts and materials

Computational approach for multi performances optimization of EDM

This paper proposes a new computational approach employed in obtaining optimal parameters of multi performances EDM. Regression and artificial neural network (ANN) are used as the modeling techniques meanwhile multi objective genetic algorithm (multiGA) is used as the optimization technique. Orthogonal array L256 is implemented in the procedure of network function and network architecture selection. Experimental studies are carried out to verify the machining performances suggested by this approach. The highest MRR value obtained from OrthoANN – MPR – MultiGA is 205.619 mg/mi n and the lowest Ra value is 0.0223µm

Practical security against differential cryptanalysis of extended feistel network

Immunity against differential cryptanalysis is an important measure in designing symmetric ciphers. Practical security is a measure to evaluate this immunity by estimating the minimum number of differential active s-boxes. A cipher with more s-boxes is said to have better immunity against differential cryptanalysis. In this paper, we evaluate the immunity of three types of Extended Feistel Network (EFN) with Substitution-Permutation (SP) round function and compare it with a balanced Feistel Network (FN). Weight-Based Representation (WBR) of sub-blocks is employed in estimating the minimum number of s-boxes of these cipher structures. The result shows that EFN Type-II and EFN Type-III have better immunity to differential cryptanalysis than a balanced FN. This is due to a difference cancellation that occurs more often in a balanced FN

Hybrid web page prediction model for predicting a user's next access

The web user sessions are clustered with incorporating the sequence of web page visits. A sequence-based clustering is developed by proposing new sequence representations and new similarity measures. The resulting sequence representation allows for calculation of similarity between web user sessions and then, can be used as input of clustering algorithms. This study proposed a hybrid prediction model (HyMFM) that integrates Markov model, Association rules and Fuzzy Adaptive Resonance Theory (Fuzzy ART) clustering together. The three approaches are integrated to maximize their strengths. A series of experiments was conducted to investigate whether, clustering performance is affected by different sequence representations and different similarity measures. This model could provide better prediction than using each approach individually